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Update README.md

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  1. README.md +11 -15
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@@ -7,10 +7,7 @@ The following code load and test the models on colab notebook.
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  ## Prerequisites
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- 1. Install the required Python libraries:
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- ```bash
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- pip install torch transformers pandas scikit-learn huggingface_hub
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-
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  ```python
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  from huggingface_hub import login
@@ -19,23 +16,22 @@ import torch.nn as nn
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  from transformers import RobertaForSequenceClassification, RobertaTokenizer
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  from torch.utils.data import Dataset, DataLoader
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  import pandas as pd
 
 
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  from sklearn.metrics import accuracy_score
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- from huggingface_hub import login
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  from transformers import AutoModel, AutoTokenizer
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- import pandas as pd
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-
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  from huggingface_hub import login
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- login("Replace with the key")
 
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- import torch
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- from torch.utils.data import Dataset, DataLoader
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- from transformers import RobertaTokenizer, RobertaForSequenceClassification
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- import pandas as pd
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- import numpy as np
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- from sklearn.metrics import accuracy_score
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- import re
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  # Define the preprocessing and dataset class
 
 
 
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  class NewsDataset(Dataset):
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  def __init__(self, texts, labels, tokenizer, max_len=128):
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  self.texts = texts
 
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  ## Prerequisites
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+ 1. Import the required Python packages:
 
 
 
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  ```python
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  from huggingface_hub import login
 
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  from transformers import RobertaForSequenceClassification, RobertaTokenizer
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  from torch.utils.data import Dataset, DataLoader
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  import pandas as pd
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+ import numpy as np
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+ import re
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  from sklearn.metrics import accuracy_score
 
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  from transformers import AutoModel, AutoTokenizer
 
 
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  from huggingface_hub import login
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+ ```
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+ 2. Log in by using the account (see our Ed private post & email sent to TAs, thanks!):
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+ ```python
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+ login("Replace with the key")
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+ ```
 
 
 
 
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  # Define the preprocessing and dataset class
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+
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+ 1. Run the following preprocessing code
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+
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  class NewsDataset(Dataset):
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  def __init__(self, texts, labels, tokenizer, max_len=128):
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  self.texts = texts